H2Hadoop : Metadata Centric BigData Analytics on Related Jobs Data Using Hadoop Pseudo Distributed Environment (original) (raw)
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Hadoop is a Framework that considers the conveyed transforming from claiming massive information sets crosswise over businesses from clusters of computer systems. Hadoop carries some confinements that would be created to have a better execution in executing jobs. These confinements are commonly as a result of facts locality inside the cluster, process and activity Scheduling, CPU execution time, or asset distributions in Hadoop.Likewise, H2Hadoop gives a productive Data Mining approach for Cloud computing situations. H2Hadoop design influences on Name Node's capacity to dole out employments to the Task Trackers (Data Nodes) inside the bunch. By adding control highlights to the Name Node, H2Hadoop can shrewdly immediate and dole out errands to the Data Nodes that contain the required information without sending the activity to the entire group. Contrasting and local Hadoop, H2Hadoop diminishes CPU time, number of read tasks, and another Hadoop factors.
Efficient Processing of Job by Enhancing Hadoop Mapreduce Framework
International Journal of Advanced Research in Computer Science
Cloud Computing uses Hadoop framework for processing BigData in parallel. The Hadoop Map Reduce programming paradigm used in the context of Big Data, is one of the popular approaches that abstract the characterstics of parallel and distributed computing which comes off as a solution to Big Data. Improving performance of Map Reduce is a major concern as it affects the energy efficiency. Improving the energy efficiency of Map Reduce will have significant impact on energy savings for data centers. There are many parameters that influence the performance of Map Reduce. Various parameters like scheduling, resource allocation and data flow have a significant impact on Map Reduce performance. Cloud Computing leverages Hadoop framework for processing BigData in parallel. Hadoop has certain limitations that could be exploited to execute the job efficiently. Efficient resource allocation remains a challenge in Cloud Computing MapReduce platforms. We propose a methodology which is an enhanced Hadoop architecture that reduces the computation cost associated with BigData analysis.
Influence of Hadoop in Big Data Analysis and Its Aspects
This paper is an effort to present the basic understanding of BIG DATA and HADOOP and its usefulness to an organization from the performance perspective. Along-with the introduction of BIG DATA, the important parameters and attributes that make this emerging concept attractive to organizations has been highlighted. The paper also evaluates the difference in the challenges faced by a small organization as compared to a medium or large scale operation and therefore the differences in their approach and treatment of BIG DATA. As Hadoop is a Substantial scale, open source programming system committed to adaptable, disseminated, information concentrated processing. A number of application examples of implementation of BIG DATA across industries varying in strategy, product and processes have been presented. This paper also deals with the technology aspects of BIG DATA for its implementation in organizations. Since HADOOP has emerged as a popular tool for BIG DATA implementation. Map reduce is a programming structure for effectively composing requisitions which prepare boundless measures of information (multi-terabyte information sets) in- parallel on extensive bunches of merchandise fittings in a dependable, shortcoming tolerant way. A Map reduce skeleton comprises of two parts. They are “mapper" and "reducer" which have been examined in this paper. The paper deals with the overall architecture of HADOOP along with the details of its various components in Big Data.
A Study on influence of Hadoop on Big Data Analytics
This paper is an effort to present the basic understanding of BIG DATA is and it's usefulness to an organization from the performance perspective. Along-with the introduction of BIG DATA, the important parameters and attributes that make this emerging concept attractive to organizations has been highlighted. The paper also evaluates the difference in the challenges faced by a small organization as compared to a medium or large scale operation and therefore the differences in their approach and treatment of BIG DATA. A number of application examples of implementation of BIG DATA across industries varying in strategy, product and processes have been presented. The second part of the paper deals with the technology aspects of BIG DATA for it's implementation in organizations. Since HADOOP has emerged as a popular tool for BIG DATA implementation, the paper deals with the overall architecture of HADOOP along with the details of it's various components. Further each of the components of the architecture has been taken up and described in detail.
Survey on Data Processing and Scheduling in Hadoop
International Journal of Computer Applications, 2015
There is an explosion in the volume of data in the world. The amount of data is increasing by leaps and bounds. The sources are individuals, social media, organizations, etc. The data may be structured, semi-structured or unstructured. Gaining knowledge from this data and using it for competitive advantage is the primary focus of all the organizations. In the last few years Big Data has found its way in almost every field, from government to private sectors, industry to academia. The major challenges associated with Big Data are data organization, modeling, data analysis and retrieval. Hadoop is a widely used software framework used for the large scale management and analysis of data. The main components of Hadoop: HDFS and MapReduce, enable the distributed storage and processing of data over a large number of commodity servers. This paper provides an overview of MapReduce and its capabilities and discusses the related issues.
Analyzing and Improving the Efficiency of Hadoop-Cluster for Big Data Analysis
The extent of Digitization is continuously increasing by leaps and bounds now a days, resulting in accumulation of large amount of data every second. The data can be a transaction, it can be a social media chat or from any other source. Processing such a Big Data is a very time consuming and tedious task. Though we have advanced systems and techniques to process this data but still there are possibilities of improvements. This paper analysis and explores such possibilities to improve the performance of Hadoop-cluster which is being used to process the big data. In this paper, we first analysis the performance of the cluster and then suggest some method to improve the overall performance of the system.
Big Data Analytics using Hadoop
International Journal of Computer Applications, 2014
This paper is an effort to present the basic understanding of BIG DATA is and it's usefulness to an organization from the performance perspective. Along-with the introduction of BIG DATA, the important parameters and attributes that make this emerging concept attractive to organizations has been highlighted. The paper also evaluates the difference in the challenges faced by a small organization as compared to a medium or large scale operation and therefore the differences in their approach and treatment of BIG DATA. A number of application examples of implementation of BIG DATA across industries varying in strategy, product and processes have been presented. The second part of the paper deals with the technology aspects of BIG DATA for it's implementation in organizations. Since HADOOP has emerged as a popular tool for BIG DATA implementation, the paper deals with the overall architecture of HADOOP alongwith the details of it's various components. Further each of the components of the architecture has been taken up and described in detail.
Critical Study of Hadoop Implementation and Performance Issues
The MapReduce model has become an important parallel processing model for largescale data-intensive applications like data mining and web indexing. Hadoop, an opensource implementation of MapReduce, is widely applied to support cluster computing jobs requiring low response time. The different issues of Hadoop are discussed here and then for them what are the solutions which are proposed in the various papers which are studied by the author are discussed here. Finally, Hadoop is not an easy environment to manage. The current Hadoop implementation assumes that computing nodes in a cluster are homogeneous in nature. Network delays due to data movement during running time have been ignored in the recent Hadoop research. Unfortunately, both the homogeneity and data locality assumptions in Hadoop are optimistic at best and unachievable at worst, introduces performance problems in virtualized data centers. The analysis of SPOF existing in critical nodes of Hadoop and proposes a metadata replication based solution to enable Hadoop high availability. The goal of heterogeneity can be achieved by a data placement scheme which distributes and stores data across multiple heterogeneous nodes based on their computing capacities. Analysts said that IT using the technology to aggregate and store data from multiple sources can create a whole slew of problems related to access control and ownership. Applications analyzing merged data in a Hadoop environment can result in the creation of new datasets that may also need to be protected.
Performance Improvement of Heterogeneous Hadoop Clusters Using MapReduce For Big Data
The problem that has occurred as a result of the increased connection between the device and the system is creating information at an exponential rate that it is becoming increasingly difficult for a possible solution for processing. Therefore, creating a platform for such advanced level data processing, which increase the level of hardware and software with bright data. In order to improve the efficiency of the Hadoop Cluster in large data collection and analysis, we have proposed an algorithm system that meets the needs of protected discrimination data in Hadoop Clusters and improves performance and efficiency. The proposed paper aims to find out the effectiveness of the new algorithm, compare, consultation, and find out the best solution for improving the big data scenario is a competitive approach. The map reduction techniques from Hadoop will help maintain a close watch on the underlying or discriminatory Hadoop clusters with insights of results as expected from the luminosity.
IJERT-Big Data Analytics with Hadoop
International Journal of Engineering Research and Technology (IJERT), 2021
https://www.ijert.org/big-data-analytics-with-hadoop https://www.ijert.org/research/big-data-analytics-with-hadoop-IJERTCONV9IS05007.pdf In this paper is attempt here the basic sympathetic of BIG DATA in addition to worth to organization as of Performance viewpoint. Together thru introduction of big data, the significant parameter as well the attribute that make emergent model attractive towardan organization that have been tinted. This document likewise evaluate differentiation in challenge face thru miniature organization while likened to small or large scale operation plus so the dissimilarity in their approach as well as dealing of big data. Numbers of submission example of completion of BD crosswise manufactures changeable in strategy, product then process has accessible. Next part of paper deal through technology aspect of BDdesigned for its performance in organization. ever meanwhile hadoop in company with the details of the a variety of components. additional each one of components of architecture have been in use moreover describe in feature.